动态加权蛋白质相互作用网络构建及其应用研究

胡赛; 熊慧军; 赵碧海; 李学勇; 王晶

doi:10.16383/j.aas.2015.c150211

动态加权蛋白质相互作用网络构建及其应用研究

doi: 10.16383/j.aas.2015.c150211

1.
长沙学院数学与计算机科学系长沙 410022

基金项目:

国家自然科学基金(11501054),湖南省自然科学基金项目(13JJ4106,14JJ3138),湖南省教育厅项目(10C0408,15C0124),湖南省科技计划项目(2010FJ3044,2015GK3072)资助

详细信息

作者简介:
胡赛长沙学院数学与计算机科学系副教授.2003年获得湖南大学数学与计量经济学院硕士学位.主要研究方向为生物信息学,概率论与数理统计,数据挖掘.E-mail:husaiccsu@163.com

熊慧军长沙学院数学与计算机科学系副教授.2003年获得湖南师范大学理学院硕士学位.主要研究方向为生物信息学.E-mail:xionghuijunccsu@163.com

李学勇长沙学院数学与计算机科学系教授.2003年获得湖南大学计算机学院硕士学位.主要研究方向为数据挖掘.E-mail:xueyongli@163.com

王晶博士,长沙学院数学与计算机科学系副教授.2009年获得湖南师范大学理学院博士学位.主要研究方向为图论.E-mail:wangjing1001@hotmail.com

通讯作者:
赵碧海博士,长沙学院数学与计算机科学系副教授.2014年获得中南大学信息学院博士学位.主要研究方向为生物信息学,数据挖掘.本文通信作者.E-mail:bihaizhao@163.com

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出版历程
- 收稿日期: 2015-04-13
- 修回日期: 2015-08-18
- 刊出日期: 2015-11-20

Construction of Dynamic-weighted Protein Interactome Network andIts Application

1.
Department of Mathematics and Computer Science, Changsha University, Changsha 410022

Funds:

Supported by National Natural Science Foundation of China (11501054), Natural Science Foundation of Hunan Province (13JJ4106, 14JJ3138), National Scientific Research Foundation of Hunan Province (10C0408, 15C0124), and Science and Technology Plan Project of Hunan Province (2010FJ3044, 2015GK3072)

摘要

摘要: 一个蛋白质可能在不同条件或不同时刻与不同的蛋白质发生相互作用,这称为蛋白质的动态特性.蛋白质在分子处理的不同阶段参与到不同的模块,与其他的蛋白质共同完成某项功能.因此, 动态蛋白质相互作用的研究有助于提高蛋白质功能预测的准确率.结合蛋白质相互作用网络和时间序列基因表达数据,构建动态蛋白质相互作用网络.为降低PPI网络中假阴性对功能预测产生的负面影响,结合结构域信息和复合物信息,预测和产生新的相互作用,并对相互作用加权.基于构建的动态加权网络,提出一种功能预测方法D-PIN (Dynamic protein interaction networks). 基于三个不同的酵母相互作用网络实验结果表明, D-PIN 方法的综合性能比现有方法提高了14%以上.结果验证了构建的动态加权蛋白质相互网络的有效性.
- 动态加权网络 /
- 功能预测 /
- 蛋白质相互作用网络 /
- 基因表达
Abstract: A protein would interact with different proteins under different conditions or at different time instants, which is the dynamic attribute of interactions. Proteins participate in different functional modules in different stages of molecular processing to perform different functions with other proteins. So, research of dynamic protein-protein interaction would contribute to the accuracy improvement of protein functions prediction. We construct a dynamic protein interaction network (D-PIN) by integrating protein-protein interaction network and time course gene expression data. To reduce the negative effect of false ''negative'' on the protein function prediction, we predict and generate some new protein interactions which combine with proteins' domain information and protein complexes information and weight all the interactions. Based on the weighted dynamic network, we propose a method for predicting protein function, named D-PIN. Experimental results compared with using three different yeast interactome networks indicate that the comprehensive performance of D-PIN is 14% higher other competing methods. Results also verify the effectiveness of the constructed dynamic-weighted protein interactome network.
- Dynamic weighted network /
- functions prediction /
- protein interactome network /
- gene expression

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参考文献(17)

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